package cn.zhukelili.hadoop;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

/**
 * @author zhangyajun
 * @create 2020-11-13 9:59
 **/
public class WordCountAppPartitioner {
    /**
     * Map: 读取输入的文件
     */
    public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> {

        LongWritable one = new LongWritable(1);

        /**
         * @param key     偏移量
         * @param value   每一行字符传
         * @param context
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            //接收到的每一行数据
            String line = value.toString();

            //按指定分隔符进行拆分
            String[] words = line.split(" ");

            context.write(new Text(words[0]), new LongWritable(Long.valueOf(words[1])));

            for (String word : words) {
                //通过上下文把map的处理结果输出
                context.write(new Text(word), one);
            }
        }
    }

    /**
     * Reduce: 归并操作
     */
    public static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
        @Override
        protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {

            long sum = 0;
            for (LongWritable value : values) {
                //求出key出现的次数总和
                sum += value.get();
            }

            //最终统计结果输出
            context.write(key, new LongWritable(sum));
        }
    }

    public static class Mypartitioner extends Partitioner<Text, LongWritable> {
        @Override
        public int getPartition(Text key, LongWritable value, int i) {
            if (key.toString().equals("xiaomi")) {
                return 0;
            }
            if (key.toString().equals("huawei")) {
                return 1;
            }
            if (key.toString().equals("iphone7")) {
                return 2;
            }

            return 3;
        }
    }

    /**
     * 定义Driver: 封装了MapReduce作业的所有信息
     *
     * @param args
     */
    public static void main(String[] args) throws Exception {

        //创建Configuration
        Configuration configuration = new Configuration();

        //准备清除已存在的输出目录
        Path outputPath = new Path(args[1]);
        FileSystem fileSystem = FileSystem.get(configuration);
        if (fileSystem.exists(outputPath)) {
            fileSystem.delete(outputPath, true);
            System.out.println("output file exists, but is has deleted");
        }
        //创建job
        Job job = Job.getInstance(configuration, "wordcount");

        //设置job处理类
        job.setJarByClass(WordCountAppPartitioner.class);

        //设置作业处理的输入路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));

        //设置map相关参数
        job.setMapperClass(MyMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

        //设置reduce相关参数
        job.setReducerClass(MyReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

        //通过job设置partitioner处理类
        job.setPartitionerClass(Mypartitioner.class);
        //设置4个reducer，每个分区一个，否则不生效
        job.setNumReduceTasks(4);

        //设置作业处理的输出路径
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

}
